bio-flow-cytometry-cytometry-qc

Comprehensive quality control for flow cytometry and CyTOF data. Covers flow rate stability, signal drift, margin events, dead cell exclusion, and batch QC. Use when assessing acquisition quality or identifying problematic samples before analysis.

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Dépôt: mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Dernière activité de la source: 4 février 2026 à 19:55
Langue détectée de SKILL.md: anglais
Étoiles: 31
Forks: 8

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npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill bio-flow-cytometry-cytometry-qc

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SKILL.md

Instructions source · Aperçu en lecture seule

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# Check for acquisition issues via flow rate
check_flow_rate <- function(ff, time_channel = 'Time') {
    expr <- exprs(ff)
    time <- expr[, time_channel]

    # Bin by time
    n_bins <- 50
    bins <- cut(time, breaks = n_bins, labels = FALSE)

    # Events per bin (flow rate proxy)
    events_per_bin <- table(bins)
    flow_rate <- as.numeric(events_per_bin)

    # Calculate metrics
    cv <- sd(flow_rate) / mean(flow_rate) * 100

    # Detect anomalies (>2 SD from mean)
    z_scores <- abs(scale(flow_rate))
    anomalies <- which(z_scores > 2)

    list(
        mean_rate = mean(flow_rate),
        cv_percent = cv,
        anomaly_bins = anomalies,
        stable = cv < 20 && length(anomalies) < 3
    )
}

flow_qc <- check_flow_rate(ff)
cat('Flow rate CV:', round(flow_qc$cv_percent, 1), '%\n')
cat('Stable:', flow_qc$stable, '\n')

# Detect signal drift over acquisition time
detect_signal_drift <- function(ff, channels, time_channel = 'Time') {
    expr <- exprs(ff)
    time <- expr[, time_channel]

    n_bins <- 20
    bins <- cut(time, breaks = n_bins, labels = FALSE)

    drift_results <- lapply(channels, function(ch) {
        bin_medians <- tapply(expr[, ch], bins, median, na.rm = TRUE)

        # Linear trend
        trend <- lm(bin_medians ~ seq_along(bin_medians))
        slope <- coef(trend)[2]
        r_squared <- summary(trend)$r.squared

        # Percent change over acquisition
        pct_change <- (tail(bin_medians, 1) - head(bin_medians, 1)) / head(bin_medians, 1) * 100

        list(
            channel = ch,
            slope = slope,
            r_squared = r_squared,
            percent_change = pct_change,
            drift_detected = abs(pct_change) > 10 && r_squared > 0.5
        )
    })

    names(drift_results) <- channels
    drift_results
}

marker_channels <- c('CD45', 'CD3', 'CD4', 'CD8')
drift <- detect_signal_drift(ff, marker_channels)
for (ch in names(drift)) {
    if (drift[[ch]]$drift_detected) {
        cat('DRIFT DETECTED:', ch, '(', round(drift[[ch]]$percent_change, 1), '%)\n')
    }
}

# Remove events at detector saturation limits
remove_margin_events <- function(ff, channels = NULL) {
    expr <- exprs(ff)

    if (is.null(channels)) {
        channels <- colnames(expr)
    }

    # Get channel ranges from FCS parameters
    params <- parameters(ff)

    margin_mask <- rep(FALSE, nrow(expr))

    for (ch in channels) {
        if (ch %in% colnames(expr)) {
            # Get max range from parameters
            idx <- match(ch, params@data$name)
            if (!is.na(idx)) {
                max_val <- params@data$range[idx]
                # Events at max or min are margin events
                margin_mask <- margin_mask | (expr[, ch] >= max_val * 0.99) | (expr[, ch] <= 0)
            }
        }
    }

    cat('Margin events:', sum(margin_mask), '(', round(mean(margin_mask) * 100, 2), '%)\n')
    ff[!margin_mask, ]
}

ff_no_margin <- remove_margin_events(ff, c('FSC-A', 'SSC-A'))

# Exclude dead cells using viability marker
exclude_dead_cells <- function(ff, viability_channel, threshold = NULL) {
    expr <- exprs(ff)
    viability <- expr[, viability_channel]

    if (is.null(threshold)) {
        # Auto-threshold using bimodal distribution
        # Dead cells have higher viability dye uptake
        threshold <- quantile(viability, 0.9)
    }

    live_mask <- viability < threshold

    cat('Total events:', length(live_mask), '\n')
    cat('Live cells:', sum(live_mask), '(', round(mean(live_mask) * 100, 1), '%)\n')
    cat('Dead cells:', sum(!live_mask), '(', round(mean(!live_mask) * 100, 1), '%)\n')

    ff[live_mask, ]
}

# Example with zombie dye
ff_live <- exclude_dead_cells(ff, 'Zombie-Aqua')

# CyTOF-specific quality metrics
cytof_qc <- function(ff) {
    expr <- exprs(ff)

    # Event length check (cell size proxy)
    if ('Event_length' %in% colnames(expr)) {
        event_length <- expr[, 'Event_length']

        # Typical single cells: 15-45
        good_length <- event_length >= 15 & event_length <= 45
        cat('Event length filter:', sum(good_length), '/', length(good_length),
            '(', round(mean(good_length) * 100, 1), '%)\n')
    }

    # DNA intercalator check (nucleated cells)
    dna_channels <- grep('(Ir191|Ir193|DNA)', colnames(expr), value = TRUE)
    if (length(dna_channels) > 0) {
        dna_signal <- rowMeans(expr[, dna_channels, drop = FALSE])

        # Cells should have DNA signal
        has_dna <- dna_signal > quantile(dna_signal, 0.1)
        cat('DNA+ events:', sum(has_dna), '(', round(mean(has_dna) * 100, 1), '%)\n')
    }

    # Gaussian parameters (if present)
    gauss_channels <- grep('(Center|Offset|Width|Residual)', colnames(expr), value = TRUE)
    if (length(gauss_channels) > 0) {
        cat('Gaussian parameters available for additional QC\n')
    }
}

cytof_qc(ff)

library(dplyr)

# Generate QC summary for batch of files
batch_qc_summary <- function(fcs_files) {
    results <- lapply(fcs_files, function(f) {
        ff <- read.FCS(f)

        # Basic metrics
        n_events <- nrow(ff)

        # Flow rate CV
        flow_qc <- check_flow_rate(ff)

        # Signal range check
        expr <- exprs(ff)
        signal_channels <- grep('(FSC|SSC)', colnames(expr), value = TRUE, invert = TRUE)
        median_signals <- apply(expr[, signal_channels, drop = FALSE], 2, median)

        data.frame(
            file = basename(f),
            events = n_events,
            flow_rate_cv = flow_qc$cv_percent,
            flow_stable = flow_qc$stable,
            median_signal = mean(median_signals, na.rm = TRUE)
        )
    })

    summary_df <- do.call(rbind, results)

    # Flag outliers
    summary_df$outlier <- with(summary_df,
        events < median(events) * 0.5 |
        events > median(events) * 2 |
        flow_rate_cv > 30
    )

    summary_df
}

# Run batch QC
fcs_files <- list.files('data/', pattern = '\\.fcs$', full.names = TRUE)
qc_summary <- batch_qc_summary(fcs_files)
print(qc_summary)

# Flag problematic samples
cat('\nSamples with QC issues:\n')
print(qc_summary[qc_summary$outlier, ])

library(ggplot2)

# Flow rate plot
plot_flow_rate <- function(ff, time_channel = 'Time') {
    expr <- exprs(ff)
    time <- expr[, time_channel]

    n_bins <- 100
    bins <- cut(time, breaks = n_bins, labels = FALSE)
    events_per_bin <- table(bins)

    plot_data <- data.frame(
        bin = as.numeric(names(events_per_bin)),
        events = as.numeric(events_per_bin)
    )

    ggplot(plot_data, aes(x = bin, y = events)) +
        geom_line() +
        geom_smooth(method = 'loess', color = 'red', se = FALSE) +
        theme_bw() +
        labs(title = 'Flow Rate Over Acquisition', x = 'Time Bin', y = 'Events per Bin')
}

# Signal stability plot
plot_signal_stability <- function(ff, channel, time_channel = 'Time') {
    expr <- exprs(ff)

    n_bins <- 50
    bins <- cut(expr[, time_channel], breaks = n_bins, labels = FALSE)
    bin_stats <- tapply(expr[, channel], bins, function(x) {
        c(median = median(x), q25 = quantile(x, 0.25), q75 = quantile(x, 0.75))
    })

    plot_data <- data.frame(
        bin = seq_along(bin_stats),
        median = sapply(bin_stats, '[', 'median'),
        q25 = sapply(bin_stats, '[', 'q25'),
        q75 = sapply(bin_stats, '[', 'q75')
    )

    ggplot(plot_data, aes(x = bin)) +
        geom_ribbon(aes(ymin = q25, ymax = q75), alpha = 0.3) +
        geom_line(aes(y = median), color = 'blue') +
        theme_bw() +
        labs(title = paste('Signal Stability:', channel), x = 'Time Bin', y = 'Intensity')
}

# Generate QC plots
p1 <- plot_flow_rate(ff)
ggsave('qc_flow_rate.png', p1, width = 10, height = 4)

p2 <- plot_signal_stability(ff, 'CD45')
ggsave('qc_signal_stability.png', p2, width = 10, height = 4)

# Generate comprehensive QC report
generate_qc_report <- function(ff, output_file = 'qc_report.txt') {
    sink(output_file)

    cat('=== FLOW CYTOMETRY QC REPORT ===\n\n')
    cat('File:', description(ff)$`$FIL`, '\n')
    cat('Date:', description(ff)$`$DATE`, '\n')
    cat('Total events:', nrow(ff), '\n\n')

    cat('--- Flow Rate ---\n')
    flow_qc <- check_flow_rate(ff)
    cat('CV:', round(flow_qc$cv_percent, 1), '%\n')
    cat('Status:', ifelse(flow_qc$stable, 'PASS', 'FAIL'), '\n\n')

    cat('--- Signal Channels ---\n')
    expr <- exprs(ff)
    for (ch in colnames(expr)[1:min(10, ncol(expr))]) {
        cat(ch, ': median =', round(median(expr[, ch]), 1), '\n')
    }

    sink()
    cat('Report saved to', output_file, '\n')
}

generate_qc_report(ff)

bio-flow-cytometry-cytometry-qc

Options d'installation

Vérifiez les fichiers source

Cytometry QC

Automated QC with flowAI

Cytometry QC

Automated QC with flowAI

Flow Rate Stability

Signal Drift Detection

Margin Events Removal

Dead Cell Exclusion

CyTOF-Specific QC

Batch QC Summary

Visualization

QC Report Generation

Related Skills

name	bio-flow-cytometry-cytometry-qc
description	Comprehensive quality control for flow cytometry and CyTOF data. Covers flow rate stability, signal drift, margin events, dead cell exclusion, and batch QC. Use when assessing acquisition quality or identifying problematic samples before analysis.
tool_type	r
primary_tool	flowAI

bio-flow-cytometry-cytometry-qc

Informations de source

Options d'installation

Vérifiez les fichiers source

Plus depuis ce dépôt

Plus depuis ce dépôt

Cytometry QC

Automated QC with flowAI

Cytometry QC

Automated QC with flowAI

Flow Rate Stability

Signal Drift Detection

Margin Events Removal

Dead Cell Exclusion

CyTOF-Specific QC

Batch QC Summary

Visualization

QC Report Generation

Related Skills